Variability in ambient ozone and fine particle concentrations and population susceptibility among Canadian health regions
Bibliographic record
Abstract
To estimate the proportion of the Canadian population that is more susceptible to adverse effects of ozone (O 3 ) and fine particle (PM 2.5 ) air pollution exposure and how this varies by health region alongside ambient concentrations of O 3 and PM 2.5 . Using data from the census, the Canadian Community Health Survey, vital statistics and published literature, we generated cross-sectional estimates for 2014 of the proportions of the Canadian population considered more susceptible due to age, chronic disease, pregnancy, outdoor work, socio-economic status, and diet. We also estimated 2010–2012 average concentrations of O 3 and PM 2.5 . Analyses were conducted nationally and for 110 health regions. Restrictive criteria (age < 10 or ≥ 75; asthma, chronic obstructive pulmonary disease, heart disease, or diabetes; pregnancy) suggested that approximately one third of the Canadian population is more susceptible, while inclusive criteria (restrictive plus age 10–19 and 65–74, outdoor work, less than high school education, low vitamin C intake) increased this proportion to approximately two thirds. Across health regions, estimates ranged from 24.4% to 41.2% (restrictive) and 61.2% to 87.0% (inclusive). Ten health regions were in the highest quartile of both population susceptibility and O 3 or PM 2.5 concentrations, all of which were outside major urban centres. A substantial proportion of the Canadian population exhibits at least one risk factor that increases their susceptibility to adverse effects of O 3 and PM 2.5 exposure. Both risk communication and management interventions need to be increasingly targeted to regions outside large urban centres in the highest quartiles of both susceptibility and exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".